



'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class 							a__( snake_case__     ):
			a_	: torch.FloatTensor
			a_	: torch.FloatTensor





class 							a__( snake_case__   ,      snake_case__     ):
			a_	: str									=      1






			@register_to_config
			def __init__(     self					,   _UpperCAmelCase = 2000					,   _UpperCAmelCase = 0.15					,   _UpperCAmelCase = 0.01					,   _UpperCAmelCase = 1_348.0					,   _UpperCAmelCase = 1E-5					,   _UpperCAmelCase = 1					,   )							->							str:
					# standard deviation of the initial noise distribution
					snake_case__	     =sigma_max

					# setable values
					snake_case__	     =None

					self.set_sigmas(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							torch.FloatTensor:
					return sample






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None						)							->							Tuple:
					snake_case__	     =sampling_eps if sampling_eps is not None else self.config.sampling_eps

					snake_case__	     =torch.linspace(1					,   _UpperCAmelCase					,   _UpperCAmelCase					,   device=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None						)							->							List[Any]:
					snake_case__	     =sigma_min if sigma_min is not None else self.config.sigma_min
					snake_case__	     =sigma_max if sigma_max is not None else self.config.sigma_max
					snake_case__	     =sampling_eps if sampling_eps is not None else self.config.sampling_eps
					if self.timesteps is None:
							self.set_timesteps(_UpperCAmelCase					,   _UpperCAmelCase						)

					snake_case__	     =sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
					snake_case__	     =torch.exp(torch.linspace(math.log(_UpperCAmelCase						)					,   math.log(_UpperCAmelCase						)					,   _UpperCAmelCase						)						)
					snake_case__	     =torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							int:
					return torch.where(
					    timesteps == 0					,   torch.zeros_like(t.to(timesteps.device						)						)					,   self.discrete_sigmas[timesteps - 1].to(timesteps.device						)					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = True					,   )							->							Union[SdeVeOutput, Tuple]:
					if self.timesteps is None:
							raise ValueError(
							    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'						)

					snake_case__	     =timestep * torch.ones(
					    sample.shape[0]					,   device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
					snake_case__	     =(timestep * (len(self.timesteps						) - 1)).long()

					# mps requires indices to be in the same device, so we use cpu as is the default with cuda
					snake_case__	     =timesteps.to(self.discrete_sigmas.device						)

					snake_case__	     =self.discrete_sigmas[timesteps].to(sample.device						)
					snake_case__	     =self.get_adjacent_sigma(_UpperCAmelCase					,   _UpperCAmelCase						).to(sample.device						)
					snake_case__	     =torch.zeros_like(_UpperCAmelCase						)
					snake_case__	     =(sigma**2 - adjacent_sigma**2) ** 0.5

					# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
					# also equation 47 shows the analog from SDE models to ancestral sampling methods
					snake_case__	     =diffusion.flatten()
					while len(diffusion.shape						) < len(sample.shape						):
							snake_case__	     =diffusion.unsqueeze(-1						)
					snake_case__	     =drift - diffusion**2 * model_output

					#  equation 6: sample noise for the diffusion term of
					snake_case__	     =randn_tensor(
					    sample.shape					,   layout=sample.layout					,   generator=_UpperCAmelCase					,   device=sample.device					,   dtype=sample.dtype						)
					snake_case__	     =sample - drift  # subtract because `dt` is a small negative timestep
					# TODO is the variable diffusion the correct scaling term for the noise?
					snake_case__	     =prev_sample_mean + diffusion * noise  # add impact of diffusion field g

					if not return_dict:
							return (prev_sample, prev_sample_mean)

					return SdeVeOutput(prev_sample=_UpperCAmelCase					,   prev_sample_mean=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = True					,   )							->							Union[SchedulerOutput, Tuple]:
					if self.timesteps is None:
							raise ValueError(
							    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'						)

					# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
					# sample noise for correction
					snake_case__	     =randn_tensor(sample.shape					,   layout=sample.layout					,   generator=_UpperCAmelCase						).to(sample.device						)

					# compute step size from the model_output, the noise, and the snr
					snake_case__	     =torch.norm(model_output.reshape(model_output.shape[0]					,   -1						)					,   dim=-1						).mean()
					snake_case__	     =torch.norm(noise.reshape(noise.shape[0]					,   -1						)					,   dim=-1						).mean()
					snake_case__	     =(self.config.snr * noise_norm / grad_norm) ** 2 * 2
					snake_case__	     =step_size * torch.ones(sample.shape[0]						).to(sample.device						)
					# self.repeat_scalar(step_size, sample.shape[0])

					# compute corrected sample: model_output term and noise term
					snake_case__	     =step_size.flatten()
					while len(step_size.shape						) < len(sample.shape						):
							snake_case__	     =step_size.unsqueeze(-1						)
					snake_case__	     =sample + step_size * model_output
					snake_case__	     =prev_sample_mean + ((step_size * 2) ** 0.5) * noise

					if not return_dict:
							return (prev_sample,)

					return SchedulerOutput(prev_sample=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							torch.FloatTensor:
					# Make sure sigmas and timesteps have the same device and dtype as original_samples
					snake_case__	     =timesteps.to(original_samples.device						)
					snake_case__	     =self.discrete_sigmas.to(original_samples.device						)[timesteps]
					snake_case__	     =(
					    noise * sigmas[:, None, None, None]
					    if noise is not None
					    else torch.randn_like(_UpperCAmelCase						) * sigmas[:, None, None, None]
					)
					snake_case__	     =noise + original_samples
					return noisy_samples






			def __len__(     self						)							->							List[Any]:
					return self.config.num_train_timesteps









'''simple docstring'''

import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


SCREAMING_SNAKE_CASE__     :						List[Any]	=  logging.get_logger(__name__)

enable_full_determinism()

class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: Dict									=      UNetaDModel
			a_	: List[Any]									=      '''sample'''






			@property
			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =4
					snake_case__	     =3
					snake_case__	     =(32, 32)

					snake_case__	     =floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10]						).to(_UpperCAmelCase						)

					return {"sample": noise, "timestep": time_step}






			@property
			def 						_lowercase  (     self						)							->							Optional[int]:
					return (3, 32, 32)






			@property
			def 						_lowercase  (     self						)							->							Optional[int]:
					return (3, 32, 32)






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     ={
					    'block_out_channels': (32, 64),
					    'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
					    'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
					    'attention_head_dim': 3,
					    'out_channels': 3,
					    'in_channels': 3,
					    'layers_per_block': 2,
					    'sample_size': 32,
					}
					snake_case__	     =self.dummy_input
					return init_dict, inputs_dict





class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: Union[str, Any]									=      UNetaDModel
			a_	: Optional[Any]									=      '''sample'''






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =4
					snake_case__	     =4
					snake_case__	     =(32, 32)

					snake_case__	     =floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10]						).to(_UpperCAmelCase						)

					return {"sample": noise, "timestep": time_step}






			@property
			def 						_lowercase  (     self						)							->							Optional[int]:
					return (4, 32, 32)






			@property
			def 						_lowercase  (     self						)							->							Dict:
					return (4, 32, 32)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ={
					    'sample_size': 32,
					    'in_channels': 4,
					    'out_channels': 4,
					    'layers_per_block': 2,
					    'block_out_channels': (32, 64),
					    'attention_head_dim': 32,
					    'down_block_types': ('DownBlock2D', 'DownBlock2D'),
					    'up_block_types': ('UpBlock2D', 'UpBlock2D'),
					}
					snake_case__	     =self.dummy_input
					return init_dict, inputs_dict






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase						)

					self.assertIsNotNone(_UpperCAmelCase						)
					self.assertEqual(len(loading_info['missing_keys']						)					,   0						)

					model.to(_UpperCAmelCase						)
					snake_case__	     =model(**self.dummy_input						).sample

					assert image is not None, "Make sure output is not None"






			@unittest.skipIf(torch_device != 'cuda'					,   'This test is supposed to run on GPU'						)
			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					snake_case__	     =model(**self.dummy_input						).sample

					assert image is not None, "Make sure output is not None"






			@unittest.skipIf(torch_device != 'cuda'					,   'This test is supposed to run on GPU'						)
			def 						_lowercase  (     self						)							->							Optional[Any]:
					# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase						)
					model_accelerate.to(_UpperCAmelCase						)
					model_accelerate.eval()

					snake_case__	     =torch.randn(
					    1					,   model_accelerate.config.in_channels					,   model_accelerate.config.sample_size					,   model_accelerate.config.sample_size					,   generator=torch.manual_seed(0						)					,   )
					snake_case__	     =noise.to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10] * noise.shape[0]						).to(_UpperCAmelCase						)

					snake_case__	     =model_accelerate(_UpperCAmelCase					,   _UpperCAmelCase						)['sample']

					# two models don't need to stay in the device at the same time
					del model_accelerate
					torch.cuda.empty_cache()
					gc.collect()

					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained(
					    'fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase					,   low_cpu_mem_usage=_UpperCAmelCase						)
					model_normal_load.to(_UpperCAmelCase						)
					model_normal_load.eval()
					snake_case__	     =model_normal_load(_UpperCAmelCase					,   _UpperCAmelCase						)['sample']

					assert torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-3						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'						)
					model.eval()
					model.to(_UpperCAmelCase						)

					snake_case__	     =torch.randn(
					    1					,   model.config.in_channels					,   model.config.sample_size					,   model.config.sample_size					,   generator=torch.manual_seed(0						)					,   )
					snake_case__	     =noise.to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10] * noise.shape[0]						).to(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   _UpperCAmelCase						).sample

					snake_case__	     =output[0, -1, -3:, -3:].flatten().cpu()
					# fmt: off
					snake_case__	     =torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]						)
					# fmt: on

					self.assertTrue(torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-3						)						)





class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: List[str]									=      UNetaDModel
			a_	: Optional[int]									=      '''sample'''






			@property
			def 						_lowercase  (     self					,   _UpperCAmelCase=(32, 32)						)							->							Tuple:
					snake_case__	     =4
					snake_case__	     =3

					snake_case__	     =floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor(batch_size * [10]						).to(dtype=torch.intaa					,   device=_UpperCAmelCase						)

					return {"sample": noise, "timestep": time_step}






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					return (3, 32, 32)






			@property
			def 						_lowercase  (     self						)							->							Optional[Any]:
					return (3, 32, 32)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ={
					    'block_out_channels': [32, 64, 64, 64],
					    'in_channels': 3,
					    'layers_per_block': 1,
					    'out_channels': 3,
					    'time_embedding_type': 'fourier',
					    'norm_eps': 1E-6,
					    'mid_block_scale_factor': math.sqrt(2.0						),
					    'norm_num_groups': None,
					    'down_block_types': [
					        'SkipDownBlock2D',
					        'AttnSkipDownBlock2D',
					        'SkipDownBlock2D',
					        'SkipDownBlock2D',
					    ],
					    'up_block_types': [
					        'SkipUpBlock2D',
					        'SkipUpBlock2D',
					        'AttnSkipUpBlock2D',
					        'SkipUpBlock2D',
					    ],
					}
					snake_case__	     =self.dummy_input
					return init_dict, inputs_dict






			@slow
			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'					,   output_loading_info=_UpperCAmelCase						)
					self.assertIsNotNone(_UpperCAmelCase						)
					self.assertEqual(len(loading_info['missing_keys']						)					,   0						)

					model.to(_UpperCAmelCase						)
					snake_case__	     =self.dummy_input
					snake_case__	     =floats_tensor((4, 3) + (256, 256)						).to(_UpperCAmelCase						)
					snake_case__	     =noise
					snake_case__	     =model(**_UpperCAmelCase						)

					assert image is not None, "Make sure output is not None"






			@slow
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'						)
					model.to(_UpperCAmelCase						)

					snake_case__	     =4
					snake_case__	     =3
					snake_case__	     =(256, 256)

					snake_case__	     =torch.ones((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor(batch_size * [1E-4]						).to(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   _UpperCAmelCase						).sample

					snake_case__	     =output[0, -3:, -3:, -1].flatten().cpu()
					# fmt: off
					snake_case__	     =torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]						)
					# fmt: on

					self.assertTrue(torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-2						)						)






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'						)
					model.to(_UpperCAmelCase						)

					snake_case__	     =4
					snake_case__	     =3
					snake_case__	     =(32, 32)

					snake_case__	     =torch.ones((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor(batch_size * [1E-4]						).to(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   _UpperCAmelCase						).sample

					snake_case__	     =output[0, -3:, -3:, -1].flatten().cpu()
					# fmt: off
					snake_case__	     =torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]						)
					# fmt: on

					self.assertTrue(torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-2						)						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					# not required for this model
					pass




